Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation

Abstract

Abstract With the advent of complex and sophisticated architectures in semiconductor device manufacturing, atomic-resolution accuracy and precision are commonly required for industrial plasma processing. This demands a comprehensive understanding of the plasma–material interactions—particularly for forming fine high-aspect ratio (HAR) feature patterns with sufficiently high yield in wafer-level processes. In particular, because the shape distortion in HAR pattern etching is attributed to the deviation of the energetic ion trajectory, the detailed ion–surface interactions need to be thoroughly investigated. In this study, molecular dynamics (MD) simulations were utilized to obtain a fundamental understanding of the collisional nature of accelerated Ar ions on the fluorinated Si surface that may appear on the sidewall of the HAR etched hole. High-fidelity data for ion–surface interaction features representing the energy and angle distributions (EADs) of sputtered atoms for varying degrees of surface F coverage and ion incident angles were obtained via extensive MD simulations. A deep learning-based reduced-order modeling (DL-ROM) framework was developed for efficiently predicting the characteristics of the ion–surface interactions. In the ROM framework, a conditional variational autoencoder (AE) was implemented to obtain regularized latent representations of the distributional data with the condition of the governing factors of the physical system. The proposed ROM framework accurately reproduced the MD simulation results and significantly outperformed various DL-ROMs, such as AE, sparse AE, contractive AE, denoising AE, and variational AE. From the inferred features of the sputtering yield and EADs of sputtered/scattered species, significant insights can be obtained regarding the ion interactions with the fluorinated surface. As the ion incident angle deviated from the glancing-angle range (incident angle >80°), diffuse reflection behavior was observed, which can substantially affect the ion transport in the HAR patterns. Moreover, it was hypothesized that a shift in sputtering characteristics occurs as the surface F coverage varies, based on the inferred EADs. This conjecture was confirmed through detailed MD simulations that demonstrated the fundamental relationship between surface atomic conformations and their sputtering behavior. Combined with additional atomistic-scale investigations, this framework can provide an efficient way to reveal various fundamental plasma–material interactions which are highly demanded for the future development of semiconductor device manufacturing.